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The study evaluates the effects of GVC participation on firm productivity and profitability. Hence this study aims to find evidence whether there is a clear difference between the productivity and profitability effects of simple and complex backward and forward participations for Turkish firms.

The authors employ a firm level data from the Türkiye's both first and second top 500 industrial enterprises from 1993 to 2019. In addition, the authors calculate country-sector level both backward and forward GVC participation indices with their simple and complex sub-indices for each year from 1990 to 2015 from the Full Eora data of the Eora Global Supply Chain Database. The authors estimate the model with OLS and fixed effects. To understand the role of the 2008 global crisis, the authors also undertake estimations for the pre-crisis and post-crisis. The authors also divide the data by R&D intensity of sectors.

While backward GVC participation lowers both labor productivity and profitability growth, forward GVC participation promotes both. Moreover, simple and complex backward participation have similarly negative effects on productivity and profitability growth, simple and complex forward participation have the completely opposite effects though. The authors then provide substantial evidence for the differing effects of participation on productivity and profitability growth between pre-crisis and post-crisis periods. Interestingly, backward participation has a negative impact for both hi-tech and low-tech firms while forward participation boosts the productivity growth only for low-tech firms, probably due to the relatively more upstream position of low-tech firms.

To the best of the knowledge, no previous study has yet examined the profitability effects of GVC for firms. Second, in addition to overall backward and forward GVC participation rates, the authors also calculate and utilize simple and complex GVC measures in the estimations. Third, to reveal whether the global financial crisis leads to a shift in the productivity and profitability effects of GVCs, the authors separately run the regressions for the pre- and post-crisis periods. Fourth, the authors then investigate the argument that hi-tech sectors/firms could benefit more from joining GVCs compared to firms in low-tech technology sectors.

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

The purpose of this study is to investigate patient perceived value as a stimulus of patient engagement behaviors both from the conceptual and empirical perspectives.

Based on the stimulus–organism–response framework, the authors developed a model to determine the impact of patient perceived value on patient engagement behavior in health care. The data were collected from a sample of 391 patients hospitalized in private hospitals. Structural equation modeling technique was used to test the research hypotheses.

The findings confirmed relevance of the service quality dimensions reliability, tangibility, responsiveness and empathy as significant antecedents of patient perceived value. Perceived value plays a significant role in shaping word of mouth and patient helping behaviors.

The findings of this study are relevant and applicable to patients in private hospitals.

This study contributes to the literature by providing new evidence on patient perceived value and engagement behaviors as a response to care quality. With adequate focus on perceived value and service quality, service providers can strengthen the relationship with patients and build a sustainable competitive advantage, by stimulating engagement behaviors in patients.

This study is of unique value to the health-care literature, both from the theoretical and managerial point of views. This study proposes a conceptual model of patient perceived value which can be used in the private health sector. Moreover, this study contributes to the health-care literature by introducing patient-helping behavior.

This study aims to demonstrate the application of Lean Six Sigma (LSS) at a stainless steel manufacturer in Türkiye for yield improvement.

A qualitative approach consisting of a single descriptive case study was adopted. Both primary and secondary sources were used. The interviews were conducted with the Six Sigma team. In addition, an in-depth review of the project documents was conducted. The “define, measure, analyze, improve and control (DMAIC)” phases were explained by examining the tables, facts and figures. The company’s downgraded rate owing to defective materials was 0.21%. Root causes were detected in the tension unit, carpet cleaning, coating unit, film surface and cleaning of the rolls. Therefore, improvements were taken accordingly.

The rolled throughput yield was 99.05%, and the defect rate was reduced to 0.08% after implementing LSS, which provided statistically proven results and a direct reflection on customer satisfaction.

To the best of the author’s knowledge, this is the first case study examining the application of LSS to improve the yield of a medium-sized stainless steel company in Türkiye.